GRE DI: How to Solve Data Interpretation Questions

Here is a simple, clear four-step approach for GRE Data Interpretation sets. Step one, take a quick look at the charts and graphs and get the macro view of what the data represents, without going into details. Step two, read the question stem and lock onto the exact requirement. Step three, identify the useful data needed for the question in hand. Step four, do the math and arrive at the exact numeric answer, then repeat steps two through four for the other two questions in the set, and cross check every answer before you confirm it. The following video, a part of our end-to-end GRE prep course, explains this four-step approach in a lucid way and helps you apply it across your GRE prep and practice across GRE drills and GRE mocks.

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Recap from the Overview of GRE DI Chapter

Recap from the Overview of GRE DI chapter.

Data Interpretation (DI) involves general quantitative questions where background information is provided through graphs, charts, and tables. The core objective is to identify relevant information from these visual aids, filter out unnecessary data, and process the useful points to answer specific questions.

Core Skills

The primary skills required for mastering this area include:

Macro-data Understanding: Grasping the overall context of the information presented.
Data Identification: Pinpointing the exact values or points needed for a specific query.
Data Conversion: The ability to take raw data and apply it to reach a final answer.

Key Mathematical Concepts

DI problems frequently test proficiency in the following academic areas:

Percentages: This includes calculating specific percentages, as well as finding percentage change and absolute change.
Ratios: Comparing different data sets or parts of a whole.
Statistics: Concepts such as Mean, Median, Mode, Range, and Standard Deviation.
Probability: Determining the likelihood of specific outcomes based on the provided data.

Strategy for Attempting Data Interpretation Sets

Summary - Strategy for Attempting Data Interpretation Sets

To master Data Interpretation (DI) sets, follow this systematic four-step strategy:

1. Understand the macro-data

Begin by grasping the overall context and structure of the information provided in the charts or tables.

2. Understand the question

Clearly identify what the specific problem is asking for before looking at the numbers.

3. Identify the useful data

Locate the specific values and variables within the dataset that are relevant to the question at hand.

4. Do the Math

Perform the necessary calculations to arrive at the solution.
For every subsequent question within the same Data Interpretation set, repeat steps 2 through

Application of Strategy: Attempting the DI Sets

To efficiently solve Data Interpretation (DI) sets, follow a structured four-step process designed to maximize speed and accuracy:

Step 1: Scan the Macro Data

Begin by obtaining a broad overview of the information presented. Focus on the big picture and the general structure of the data sets rather than getting bogged down in minor nuances or details at the start.

Step 2: Analyze the First Question

Read the first question before diving deep into the data. Identify two key elements:

What exactly is being asked?
What specific data is required to formulate the answer?

Step 3: Extract Useful Data

Filter through the information to isolate only the necessary data points. Recognize that not all data provided in the set is useful for every question. Pinpoint the specific figures or trends required for the current problem.

Step 4: Execute and Solve

Perform the necessary calculations. Determine if a quick approximation is sufficient based on the answer choices, or if a precise value is required.

Correct Approach for Mastering Data Interpretation for GRE

To master data interpretation, one must follow a structured approach centered on building a strong quantitative foundation and engaging in a rigorous feedback loop of practice and review.

Fundamental Approach

Build a Quantitative Base: Develop a solid understanding across all quantitative concepts and topics. This foundational knowledge is essential before moving into complex data sets.
Extensive Practice: Engage in consistent practice to develop a knack for synthesizing information and using data specifically to answer targeted questions.
Error Analysis: Systematically analyze incorrect attempts. It is vital to re-attempt these specific questions after a few weeks to ensure the underlying concept has been mastered.

Key Principles for Mastery

Learning from Errors: Mistakes should be viewed as tools for improvement rather than failures.
Application Over Theory: Beyond a certain level of foundational knowledge, progress is driven by practicing extensively and analyzing mistakes rather than simply studying more theory.